We evaluate the average performance of NFM with different model architectures on CIFAR-10 [1]}, CIFAR-100 [1]}, and ImageNet [3]}. We use a pre-activated residual network (ResNet) with depth 18 [4]} and a compact vision transformer (ViT-lite) with 7 attention layers and 4 heads [5]} on small scale tasks. For more challenging and higher dimensional tasks, we consider the performance of wide ResNet-18 [6]} and ResNet-50 architectures, respectively.
